Segmentation algorithm of coal slurry foam with double-point directional extension based on the improved FCM clustering algorithm

• Autorzy: Wu ZiHao , Huang XianWu , Shang HaiLi , Zhao YuHong

Identyfikatory

DOI

10.37190/ppmp/158850

• Języki publikacji: EN

Abstrakty

EN

In flotation production, the visual surface information of the flotation foam reflects the flotation effects, which are closely related to the flotation conditions and directly reflect the degree of mineralization of the foam layer. In this study, it was proposed a novel and efficient segmentation algorithm to extract the edge information of slime bubbles, as the boundaries are typically blurred and difficult to segment, due to the slime bubbles sticking to each other in the slime flotation foam image. First, the improved clustering algorithm and image morphology operation were used to extract the edges of the foam spots. Second, the image morphological operations were used as a starting point to look around the foam edge points. The pseudo-edge points were then removed using a region and spatial removal algorithm. Finally, the foam edges were extracted using the double-point directed expansion algorithm. A new criterion was proposed for segmentation effect determination based on the particularity of the segmented object. The feasibility and effectiveness of the foam segmentation method were investigated by comparative experiments. The experimental results showed that the proposed algorithm could obtain the foam surface properties more accurately and provide effective guidance for flotation production.

Słowa kluczowe

EN

flotation; slime foam segmentation; FCM; clustering algorithm; double-point directional extension; segmentation effect judgment standard

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2023
• Tom: Vol. 59, iss. 1
• Strony: art. no. 158850
• Opis fizyczny: Bibliogr. 48 poz., rys., tab., wykr.

Twórcy

autor

Wu ZiHao

• School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, China

autor

Huang XianWu

• School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, China

autor

Shang HaiLi

• Key Laboratory of Coal Processing and Efficient Utilization, (China University of Mining and Technology), Ministry of Education, Xu Zhou China

autor

Zhao YuHong

• School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, China

Bibliografia

• ALDRICH, C., FENG, D. 2000. Effect of frothers on bubble size distributions in flotation pulp phases and surface froths. Minerals Engineering, 13(10), 1049-1057.
• ALDRICH, C., MARAIS, C., SHEAN, B.J., CILLIERS, J.J. 2010. Online monitoring and control of froth flotation systems with machine vision: A review. International Journal of Mineral Processing, 96(1-4):1-13.
• ARYASUTA, N., JENA, M.S.& MANDRE, N.R. 2022. Beneficiation of lead-zinc ores – A Review, Mineral Processing and Extractive Metallurgy Review, 43:5, 564-583.
• BACKES, A.R., CASANOVA, D., BRUNO, O.M. 2013. Texture analysis and classification: A complex network-based approach. Information Sciences, 219(Complete), 168-180.
• BHONDAYI, C. 2022. Flotation froth phase bubble size measurement. Mineral Processing and Extractive Metallurgy Review, 43:2, 251-273.
• CAO, W., QIAO, Z., GAO, Z., LU, S., TIAN, F. 2021. Use of unmanned aerial vehicle imagery and a hybrid algorithm combining a watershed algorithm and adaptive threshold segmentation to extract wheat lodging. Physics and Chemistry of the Earth, Parts A/B/C, 123, 103016.
• CHO, Y.S., LASKOWSKI, J.S. 2002. Effect of flotation frothers on bubble size and foam stability. International Journal of Mineral Processing, 64(2-3), 69-80.
• CHUDASAMA, D., PATEL, T.A., JOSHI, S., PRAJAPATI, G. 2015. Image segmentation using morphological operations. International Journal of Computer Applications, 117(18).
• DENG, Q., ZHAN, Y., LIU, C., QIU, Y., ZHANG, A. 2021. Multiscale power spectrum analysis of 3D surface texture for prediction of asphalt pavement friction. Construction and Building Materials, 293, 123506.
• DENIZ, V., UMUCU, Y., DENIZ, O.T. 2022. Estimation of grade and recovery in the concentration of barite tailings by the flotation using the MLR and ANN analyses. Physicochemical Problems of Mineral Processing, 58(5).
• FENG, S. 2022. Effective document image binarization via a convex variational level set model. Applied Mathematics and Computation, 419, 126861.
• GARCÍA-FERNÁNDEZ, Á.F., RALPH, J., HORRIDGE, P., MASKELL, S. 2021. A Gaussian filtering method for multitarget tracking with nonlinear/non-Gaussian measurements. IEEE Transactions on Aerospace and Electronic Systems, 57(5): 3539-3548.
• HAN, S., XI, S., GENG, W. 2017. Low-resolution expression recognition based on central oblique average CS-LBP with adaptive threshold. Optoelectronics Letters, 13(6), 444-447.
• HE, D.C., WANG, L. 1990. Texture unit, texture spectrum, and texture analysis. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 509-512.
• HUIFENG, R., CHUNHUA, Y., XUAN, Z., WEIHUA, G., FENG, Y. 2011. Morphological characteristics extraction of mineral flotation froth based on adaptive edge detection. Control and Instruments in Chemical Industry, (7), 5.
• JING, J., LIU, S., WANG, G., ZHANG, W., SUN, C. 2022. Recent advances on image edge detection: A comprehensive review. Neurocomputing.
• JIAN, L., JIUSI, W., JIANGPING, L., AIPING, K., YANLING, J. 2008. Application of surfactants in foam flotation separation. Advances in Fine Petrochemicals, 9(6), 4.
• JING, W., SHUTING, C., SHIYIN, L., ZHAOLIN, L. 2017. Research on image segmentation algorithm of coal flotation froth. Coal Technology, (3):3.
• JONES, R., SVALBE, I. 1994. Algorithms for the decomposition of gray-scale morphological operations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 581-588.
• JOSE, F. O. & RUPEN, A. 1992. Physico-chemical factors affecting the separation of cassiterite and fluorite by froth flotation. Mineral Processing and Extractive Metallurgy Review, 9:1-4, 125-134.
• JUN, Z., CHAO, W., SHUAI, W., WEI, H. 2020. Combining improved watershed algorithm with k-means method in image segmentation. Journal of Chongqing University of Technology (Natural Science), 34(4), 7.
• KAIJUN, Z. 2010. Froth image morphological characteristic extraction method and its application for mineral flotation. Central South University.
• KARUNKUZHALI, D., MEENAKSHI, B., LINGAM, K. 2022. An adaptive fuzzy c means with seagull optimization algorithm for analysis of WSNs in agricultural field with IoT. Wireless Personal Communications, 1-22.
• KIM, H., AHN, E., CHO, S., ET AL. 2017. Comparative analysis of image binarization methods for crack identification in concrete structures. Cement and Concrete Research, 2017, 99, 53-61.
• LAVANYA, D.N., THIRUMURUGAN, P. 2022. Cervical cancer classification from pap smear images using modified fuzzy C means, PCA, and KNN. IETE Journal of Research, 68(3), 1591-1598.
• LIKAS, A., VLASSIS, N., VERBEEK, J.J. 2003. The global k-means clustering algorithm. Pattern recognition, 36(2): 451-461.
• LIU, Y.Y., LI, Y.P., ZHU, T.T, ET AL. 2011. Imaging study of surfactant effect on bubble behaviors in froth flotation. International Symposium on Water Resource and Environmental Protection. IEEE, 2, 1199-1202.
• LUO, J., TANG, Z., ZHANG, H., FAN, Y. 2019. A new flotation froth texture feature extraction method based on PCA, IEEE Symposium Series on Computational Intelligence (SSCI). IEEE.
• MENG, F., WANG, X., SHAO, F., WANG, D., YU, Y.W., XIAO, Y. 2021. Visual-attention gabor filter based online multi-armored target tracking. Defence Technology, 17(4), 1249-1261.
• MURPHY, D.G., ZIMMERMAN, W., WOODBURN, E.T. 1996. Kinematic model of bubble motion in a flotation froth. Powder Technology, 87(1), 3-12.
• NAKHAEI, F., IRANNAJAD, M., MOHAMMADNEJAD, S. 2019. Column flotation performance prediction: PCA, ANN and image analysis-based approaches. Physicochemical Problems of Mineral Processing, 55.
• NG, H.P., ONG, S.H., FOONG, K.W.C., GOH, P.S., NOWINSKI, W.L. 2006. Medical image segmentation using k-means clustering and improved watershed algorithm. IEEE southwest symposium on image analysis and interpretation. IEEE, 61-65.
• PANG, Y., SUN, M., JIANG, X., LI, X. 2017. Convolution in convolution for network in network. IEEE transactions on neural networks and learning systems, 29(5), 1587-1597.
• PARK, H., BAI, C., WANG, L. 2022. A convolutional neural network for classification of froth mobility in an industrial flotation cell. Mineral Processing and Extractive Metallurgy Review, 1-9.
• PARK, H., NG, C.Y., WANG, L. 2022. Bubble size in a flotation column with oscillatory air supply in the presence of frothers[J]. Mineral Processing and Extractive Metallurgy Review, 43(7), 926-934.
• PIERRE, D.A. 1986. Optimization theory with applications. Courier Corporation.
• SI, S.H., HU, F.Y., GU, Y.J., XIAN, X.F. 2014. Improved denoising algorithm based on non-regular area Gaussian filtering. Computer Science, 41, 313-316.
• SU, Z., LIU, W., YU, Z., HU, D., LIAO, Q., TIAN, Q., PIETIKAINEN, M., LIU, L. 2021. Pixel difference networks for efficient edge detection. Proceedings of the IEEE/CVF International Conference on Computer Vision. 5117-5127.
• UPLAONKAR, D.S., PATIL, N. 2021. Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm. International Journal of Intelligent Computing and Cybernetics.
• KALYANI, V.K., PALLAVIKA, SANJAY CHAUDHURI, GOURI CHARAN, T., HALDAR, D.D., KAMAL, K.P., BADHE, Y.P., TAMBE, S. S. & KULKARNI, B.D. 2007. Study of a laboratory-scale froth flotation process using artificial neural networks. Mineral Processing and Extractive Metallurgy Review, 29:2, 130-142.
• VALLEBUONA, G., CASALI, A., RODRÍGUEZ, F., ENDARA, D. 2008. Bubbles size distribution in mechanical flotation cells at laboratory and industrial scale and modeling of the effects of the operating variables. Revista De Metalurgia, 44(3), 233-242.
• WANG, F., WANG, Y., LU, M., ET AL. 2001. Bubble feature extracting based on image processing of coal flotation froth. Zhongguo Kuangye Daxue Xuebao (Journal of China University of Mining and Technology), 30.
• WANG, G., HE, T., KUANG, Y., LIN, Z. 2020. Optimization of soft-sensing model for ash content prediction of flotation tailings by image features tailings based on GA-SVMR. Physicochemical Problems of Mineral Processing, 56(4).
• WANG, W., WANG, L. 2000. Froth image segmentation algorithms and their validation. WCC 2000-ICSP 2000. 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000. IEEE, 3, 2042-2045.
• YUAN, B., XIA, B., ZHANG, D. 2018. Polarization image texture feature extraction algorithm based on CS-LBP operator. Procedia Computer Science, 131:295-301.
• ZHENG, Z., WANG, P., LIU, W., LI, J., YE, R., REN, D. 2020. Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI conference on artificial intelligence. 34(07), 12993-13000.
• ZHOU J, YANG M. 2022. Bone region segmentation in medical images based on improved watershed algorithm. Computational Intelligence and Neuroscience, 2022.
• ZIOU, D., TABBONE, S. 1998. Edge detection techniques-an overview. Pattern Recognition and Image Analysis C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii, 8, 537-559.

Uwagi

The study was financially supported by the Key Laboratory of Coal Processing and Efficient Utilization, (China University of Mining and Technology), the Ministry of Education, the Research Program of science and technology at the Universities of Inner Mongolia Autonomous Region (NJZY22451), and the Fundamental Research Funds for Inner Mongolia University of Science & Technology.